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Zero To Mastery

AI Engineering: Build, Train, Fine-Tune and Deploy Models with AWS SageMaker

via Zero To Mastery

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Overview

Launch your career in AI with this course that teaches you to build, train, fine-tune and deploy your own AI models using two of the most important AI tools used in the real world: AWS SageMaker and Hugging Face. No machine learning knowledge required!
  • Build and deploy cutting-edge artificial intelligence & machine learning models to the cloud
  • Utilize powerful pre-trained models from Hugging Face with AWS SageMaker
  • Uncover the mathematical secrets behind how Large Language Models work with a deep-dive into the Transformer architecture, tokenization, and more
  • Customize models to meet the needs of your AI applications using PyTorch to create unique solutions
  • Train and test models, ensuring they deliver accurate results every time
  • Learn best practices for monitoring and optimizing your models, including load testing and scaling for massive user demand

Syllabus

  •   Section 0: Introduction
    • AI Engineering Bootcamp: Learn AWS SageMaker with Patrik Szepesi
    • Course Setup
    • Course Introduction Part 1: Build, Train, and Deploy Models with AWS SageMaker
    • Course Introduction Part 2: Fine-Tuning LLMs with QLoRA, AWS, and Open Source
    • Exercise: Meet Your Classmates and Instructor
    • Course Resources
    • Understanding Your Video Player
    • Set Your Learning Streak Goal
  •   Part 1: Build, Train and Deploy Models with AWS SageMaker
    • Starting Point of Part 1
  •   Section 1: Introduction to AWS, Environment Setup, and Best Practices
    • Setting Up Our AWS Account
    • Set Up IAM Roles + Best Practices
    • AWS Security Best Practices
    • Set Up AWS SageMaker Domain
    • UI Domain Change
    • Sagemaker Domain Creation Update Part 1
    • Sagemaker Domain Creation Update Part 2
    • Sagemaker Notebooks Update
    • Setting Up SageMaker Environment
    • SageMaker Studio and Pricing
    • Let's Have Some Fun (+ More Resources)
  •   Section 2: Possible Resource Limit Errors Before Training and Deployment
    • Quota Increase
  •   Section 3: A Gentle Introduction to HuggingFace in SageMaker
    • Setup: SageMaker Server + PyTorch
    • HuggingFace Models, Sentiment Analysis, and AutoScaling
  •   Section 4: Gathering a Dataset for Our Multiclass Text Classification Project
    • Get Dataset for Multiclass Text Classification
    • Link to the Dataset
    • Creating Our AWS S3 Bucket
    • Uploading Our Training Data to S3
    • Unlimited Updates
  •   Section 5: Exploratory Data Analysis
    • Exploratory Data Analysis - Part 1
    • Exploratory Data Analysis - Part 2
    • Data Visualization and Best Practices
  •   Section 6: Setting Up Our Training Notebook
    • Setting Up Our Training Job Notebook + Reasons to Use SageMaker
    • Python Script for HuggingFace Estimator
  •   Section 7: Introduction to Tokenizations and Encodings
    • Creating Our Optional Experiment Notebook - Part 1
    • Creating Our Optional Experiment Notebook - Part 2
    • Encoding Categorical Labels to Numeric Values
    • Understanding the Tokenization Vocabulary
    • Encoding Tokens
    • Practical Example of Tokenization and Encoding
    • Course Check-In
  •   Section 8: Setting Up Data Loading with PyTorch
    • Creating Our Dataset Loader Class
    • Setting Pytorch DataLoader
    • Implement a New Life System
  •   Section 9: Choose Your Path
    • Which Path Will You Take?
  •   Section 10: Mathematics Behind Large Language Models and Transformers
    • DistilBert vs. Bert Differences
    • Embeddings In A Continuous Vector Space
    • Introduction To Positional Encodings
    • Positional Encodings - Part 1
    • Positional Encodings - Part 2 (Even and Odd Indices)
    • Why Use Sine and Cosine Functions
    • Understanding the Nature of Sine and Cosine Functions
    • Visualizing Positional Encodings in Sine and Cosine Graphs
    • Solving the Equations to Get the Values for Positional Encodings
    • Introduction to Attention Mechanism
    • Query, Key and Value Matrix
    • Getting Started with Our Step by Step Attention Calculation
    • Calculating Key Vectors
    • Query Matrix Introduction
    • Calculating Raw Attention Scores
    • Understanding the Mathematics Behind Dot Products and Vector Alignment
    • Visualizing Raw Attention Scores in 2D
    • Converting Raw Attention Scores to Probability Distributions with Softmax
    • Normalization
    • Understanding the Value Matrix and Value Vector
    • Calculating the Final Context Aware Rich Representation for the Word "River"
    • Understanding the Output
    • Understanding Multi Head Attention
    • Multi Head Attention Example and Subsequent Layers
    • Masked Language Learning
    • Exercise: Imposter Syndrome
  •   Section 11: Customizing our Model Architecture in PyTorch
    • Getting Back to SageMaker!
    • Creating Our Custom Model Architecture with PyTorch
    • Adding the Dropout, Linear Layer, and ReLU to Our Model
  •   Section 12: Creating the Accuracy, Training, and Validation Function
    • Creating Our Accuracy Function
    • Creating Our Train Function
    • Finishing Our Train Function
    • Setting Up the Validation Function
  •   Section 13: Optimizer Functions, Model Parameters, Cross Entropy Loss Function
    • Passing Parameters In SageMaker
    • Setting Up Model Parameters For Training
    • Understanding The Mathematics Behind Cross Entropy Loss
    • Finishing Our Script.py File
  •   Section 14: Starting Our Training Job and Monitoring it in AWS CloudWatch
    • Starting Our Training Job
    • Debugging Our Training Job With AWS CloudWatch
    • Analyzing Our Training Job Results
    • Link to the Finished Trained Model
  •   Section 15: Deploying our Multiclass Text Classification Endpoint in SageMaker
    • Creating Our Inference Script For Our PyTorch Model
    • Finishing Our PyTorch Inference Script
    • Setting Up Our Deployment
    • Deploying Our Model To A SageMaker Endpoint
  •   Section 16: Load Testing Our Machine Learning Model
    • Introduction to Endpoint Load Testing
    • Information about the next Lesson
    • Creating Our Test Data for Load Testing
    • Upload Testing Data to S3
    • Creating Our Model for Load Testing
    • Starting Our Load Test Job
    • Analyze Load Test Results
  •   Section 17: Production Grade Deployment of Our Machine Learning Model
    • Deploying Our Endpoint
    • Creating Lambda Function to Call Our Endpoint
    • Setting Up Our AWS API Gateway
    • Testing Our Model with Postman, API Gateway and Lambda
  •   Section 18: Cleaning Up Resources
    • Finishing Part 1 and Cleaning Up Resources for Part 1
  •   Part 2: Fine-Tuning LLMs with QLoRA, AWS, and Open Source
    • Starting Point of Part 2
  •   Setting Up AWS Sagemaker Environment
    • IAM Setup
    • Creating a SageMaker Domain
    • Logging in to our SageMaker Environment
    • Introduction to JupyterLab
    • Let's Have Some Fun (+ More Resources)
  •   Gathering, Chunking, Tokenizing and Uploading our Dataset
    • Sagemaker Sessions, Regions, and IAM Roles
    • Examining Our Dataset from HuggingFace
    • Tokenization and Word Embeddings
    • HuggingFace Authentication with Sagemaker
    • Applying the Templating Function to our Dataset
    • Attention Masks and Padding
    • Star Unpacking with Python
    • Chain Iterator, List Constructor and Attention Mask example with Python
    • Understanding Batching
    • Slicing and Chunking our Dataset
    • Creating our Custom Chunking Function
    • Tokenizing our Dataset
    • Running our Chunking Function
    • Understanding the Entire Chunking Process
    • Uploading the Training Data to AWS S3
    • Course Check-In
  •   Understanding LoRA and Setting up HuggingFace Estimator
    • Setting Up Hyperparameters for the Training Job
    • Creating our HuggingFace Estimator in Sagemaker
    • Introduction to Low-rank adaptation (LoRA)
    • LoRA Numerical Example
    • LoRA Summarization and Cost Saving Calculation
    • (Optional) Matrix Multiplication Refresher
    • Understanding LoRA Programatically Part 1
    • Understanding LoRA Programatically Part 2
    • Unlimited Updates
  •   Improving Training Speed with Bfloat 16
    • Bfloat16 vs Float32
    • Comparing Bfloat16 Vs Float32 Programatically
    • Implement a New Life System - at end of 3rd section
  •   Setting up the QLoRA Training Script with Mixed Precision & Double Quantization
    • Setting up Imports and Libraries for the Train Script
    • Argument Parsing Function Part 1
    • Argument Parsing Function Part 2
    • Understanding Trainable Parameters Caveats
    • Introduction to Quantization
    • Identifying Trainable Layers for LoRA
    • Setting up Parameter Efficient Fine Tuning
    • Implement LoRA Configuration and Mixed Precision Training
    • Understanding Double Quantization
    • Creating the Training Function Part 1
    • Creating the Training Function Part 2
    • Exercise: Imposter Syndrome
    • Finishing our Sagemaker Script
    • Gaining Access to Powerful GPUs with AWS Quotas
    • Final Fixes Before Training
  •   Running our Fine Tuning Script for our LLM
    • Starting our Training Job
    • Inspecting the Results of our Training Job and Monitoring with Cloudwatch
  •   Deploying our Fine Tuned LLM
    • Deploying our LLM to a Sagemaker Endpoint
    • Testing our LLM in Sagemaker Locally
    • Creating the Lambda Function to Invoke our Endpoint
    • Creating API Gateway to Deploy the Model Through the Internet
    • Implementing our Streamlit App
    • Streamlit App Correction
  •   Cleaning up Resources
    • Congratulations and Cleaning up AWS Resources
  •   Where To Go From Here?
    • Thank You!
    • Review This Course!
    • Become An Alumni
    • Learning Guideline
    • ZTM Events Every Month
    • LinkedIn Endorsements

Taught by

Patrik Szepesi

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